Header image for the post titled Real-World Evidence: The Introduction

The late 1990s and early 2000s saw the widespread adoption of electronic health records (EHRs), which made the vast amounts of real-world patient data much more accessible to researchers. At the same time, the limitations of the classic Randomized Controlled Trials (RCT) were becoming more and more apparent: the highly controlled conditions of these trials did not always reflect the real-world usage of interventions, and their strict inclusion/exclusion criteria lead to study populations that did not represent the full diversity of patient groups who will ultimately use the drug. And with the rising healthcare costs, the need to demonstrate the value of new treatments in the real-world setting became increasingly important.

The emergence of Real-World Data (RWD) and Real-World Evidence (RWE) concepts has revolutionized how drug efficacy and safety are evaluated, how clinical trials are designed, and how healthcare decisions are made. In this note, we will cover the definitions, applications, and implications of RWD and RWE in pharmaceutical research.

In December 2016, the United States Congress signed into law the “21st Century Cures Act” that formally defined and legitimized the use of RWE in regulatory decision-making. This landmark legislation required the FDA to establish a program to evaluate the potential use of RWE to support approval of new indications for approved drugs or to satisfy post-approval study requirements. Subsequently, the FDA released a framework for its RWE program in 2018, outlining how the agency would evaluate RWE for regulatory purposes. This legislative and regulatory groundwork has been crucial in accelerating the adoption of RWE in drug development and approval processes, not only in the US but globally, as other regulatory agencies have followed suit in recognizing the value of RWE.

Real World Data (RWD)

Real World Data refers to health-related information collected outside the controlled environment of randomized clinical trials. Sources of RWD include:

  1. Electronic Health Records (EHRs)
  2. Claims and billing data
  3. Product and disease registries
  4. Patient-generated data (including from in-home-use settings)
  5. Data gathered from other sources that can inform on health status, such as mobile devices

RWD provides a more comprehensive view of patient experiences and outcomes in real-world settings, offering insights that may not be captured in traditional clinical trials.

Real World Evidence (RWE)

Real World Evidence is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD. RWE can be generated by different study designs or analyses, including but not limited to:

  1. Randomized trials (including large simple trials, pragmatic trials)
  2. Observational studies (prospective and/or retrospective)

RWE complements the evidence generated from traditional randomized controlled trials (RCTs) and can provide a more complete picture of a drug’s effectiveness and safety profile in diverse patient populations.

Large Simple Trials (LSTs)

Large Simple Trials are a type of clinical trial designed to bridge the gap between traditional RCTs and real-world clinical practice. Key characteristics of LSTs include:

  • Large sample sizes: Often involving thousands or tens of thousands of patients
  • Broad inclusion criteria: To better represent the diverse patient populations that will use the drug in real-world settings
  • Simplified protocol: Focusing on a few critical outcomes rather than numerous detailed measurements
  • Streamlined data collection: Gathering only essential data points to reduce burden on participants and researchers
  • Integration with routine clinical care: Often conducted within the framework of usual patient care to increase relevance and reduce costs

LSTs are particularly useful for:

  • Detecting rare but serious adverse events
  • Comparing the effectiveness of different treatments in real-world settings
  • Assessing long-term outcomes in large populations

Pragmatic Trials

Pragmatic trials are designed to evaluate the effectiveness of interventions in real-life routine practice conditions. Unlike explanatory trials, which aim to test efficacy under ideal conditions, pragmatic trials seek to inform decision-makers about the real-world benefits and risks of a treatment. Key features of pragmatic trials include:

  • Diverse participant population: Including patients with comorbidities who might be excluded from traditional RCTs
  • Flexible intervention protocol: Allowing for variations in how the intervention is delivered, as would occur in routine practice
  • Comparison with usual care: Often comparing the new intervention to the current standard of care rather than a placebo
  • Patient-relevant outcomes: Focusing on outcomes that matter to patients and clinicians in everyday practice
  • Minimal extra data collection: Relying on routinely collected data where possible to reduce the burden on participants and healthcare providers

Pragmatic trials are particularly valuable for:

  • Assessing the real-world effectiveness of treatments
  • Informing clinical and policy decision-making
  • Understanding how interventions perform across different healthcare settings and patient subgroups

Both large simple trials and pragmatic trials play crucial roles in generating Real World Evidence. They provide a bridge between the rigorously controlled environment of traditional RCTs and the complexities of real-world clinical practice. By doing so, they offer valuable insights into the effectiveness, safety, and optimal use of medical interventions in the populations and settings where they will ultimately be used.

Impact on the Pharmaceutical Industry

The integration of RWD and RWE is significantly affecting the pharmaceutical industry in several ways:

  1. Drug Development: RWD can inform the design of clinical trials by providing insights into disease prevalence, patient populations, and current treatment patterns.

  2. Regulatory Decision-Making: Regulatory bodies like the FDA are increasingly considering RWE in their approval processes, potentially accelerating the path to market for new drugs.

  3. Post-Market Surveillance: RWD allows for more comprehensive monitoring of drug safety and effectiveness after market launch.

  4. Value Demonstration: RWE can help demonstrate the value of drugs in real-world settings, which is crucial for negotiations with payers and health technology assessment bodies.

  5. Personalized Medicine: Analysis of RWD can reveal subgroups of patients who respond particularly well to certain treatments, advancing the field of personalized medicine.

Challenges and Considerations

While RWD and RWE offer immense potential, there are several challenges to consider:

  1. Data Quality: Ensuring the quality, reliability, and relevance of RWD is crucial.

  2. Data Integration: Combining data from diverse sources can be complex and requires sophisticated data management systems.

  3. Privacy and Ethics: Collecting and using patient data raises important privacy and ethical considerations.

  4. Methodological Rigor: Developing robust methodologies for analyzing RWD and generating reliable RWE is an ongoing challenge.

  5. Regulatory Acceptance: While regulators are increasingly open to RWE, standards for its use in regulatory decision-making are still evolving.

Strategies for Effective Incorporation of RWE

To effectively leverage RWD and RWE, pharmaceutical companies should consider the following strategies:

  1. Invest in Data Infrastructure: Develop robust systems for collecting, storing, and analyzing large volumes of diverse data.

  2. Foster Collaborations: Partner with healthcare providers, technology companies, and academic institutions to access diverse data sources and analytical expertise.

  3. Develop In-House Expertise: Build teams with skills in data science, epidemiology, and health outcomes research.

  4. Engage with Regulators: Maintain open dialogue with regulatory bodies to understand and shape the evolving landscape of RWE use in drug development and approval.

  5. Prioritize Data Quality: Implement rigorous processes for data validation and quality assurance.

  6. Integrate RWE Throughout the Product Lifecycle: From early development to post-market surveillance, look for opportunities to generate and use RWE.

  7. Ensure Ethical Use of Data: Develop clear policies and procedures for ethical data collection and use, ensuring patient privacy and consent.

Wrap-up

Real World Evidence is a powerful tool that has the potential to transform pharmaceutical research and development. By providing insights into the real-world effectiveness and safety of drugs, it can accelerate innovation, improve patient outcomes, and increase the efficiency of healthcare systems. However, realizing this potential requires careful navigation of technical, ethical, and regulatory challenges. As the field continues to evolve, pharmaceutical companies that effectively incorporate RWE into their research and development processes will be well-positioned to lead in an increasingly data-driven healthcare landscape.